Distilling Cognitive Backdoor Patterns within an Image: A SOTA Method for Backdoor Sample Detection

Code for ICLR 2023 Paper "Distilling Cognitive Backdoor Patterns within an Image"

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Use Cognitive Distilation on a pretrained model and images.

• lr: the learning rate (step size) for extracting the mask.
• p: the L_p norm constraint of the mask.
• gamma (alpha used in the paper) and beta: hyperparameters for the objective function.
• num_steps*: number of steps for extracting the mask.

from cognitive_distillation import CognitiveDistillation

images = # batch of images (torch.Tensor) [b,c,h,w]
model = # a pre-trained model (torch.nn.Module)

cd = CognitiveDistillation(lr=0.1, p=1, gamma=0.01, beta=10.0, num_steps=100)
masks = cd(model, images) # the extracted masks (torch.Tensor) [b,1,h,w]
cognitive_pattern = images * masks # extracted cognitive pattern (torch.Tensor) [b,c,h,w]

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Visualizations of the masks and Cognitive Patterns

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Reproduce results from the paper

• Configurations for each experiment are stored in configs/ folder.
• Trigger patterns can be downloaded from NAD GitHub repo
• ISSBA poisoned data can be downloaded from ISSBA GitHub repo
• Dynamic attack generator can be downloaded from Dyanamic Attack GitHub repo
• For DFST attack, data can be generated from DFST GitHub repo
• Other triggers (trigger folder in this repo) can be downloaded from this Google Drive
• Frequency detector model weights can be downloaded from this Google Drive. Note that this model is trained on the GTSRB dataset (reproduced using PyTorch), based on frequency-backdoor.

Train a model

• $exp_path: the path where you want to store experiment results, checkpoints, logs
• $exp_config: where the experiment config is located
• $exp_name: name of the specific experiment configurations (*.yaml)

python train.py --exp_path $exp_path \
 --exp_config $exp_config \
 --exp_name $exp_name

Run detections

The following command will save the detection results (e.g., masks of Cognitive Distillation, a confidence score for other baselines) to $exp_path.

• --method argument specifies detection methods ['CD', 'ABL', 'Feature', 'FCT', 'STRIP'].
• $gamma is the hyperparameter value for Cognitive Distillation
• 'Feature' is used for extract deep features (used by AC and SS).
• ABL does not need to run detection. All training losses are stored in the $exp_path.

python extract.py --exp_path $exp_path \
 --exp_config $exp_config \
 --exp_name $exp_name \
 --method "CD" --gamma $gamma

Run detections

The following command will check AUPRC/AUROC for the detection results.

• --method argument specifies detection methods ['CD', 'AC', 'ABL', 'FCT', 'Frequency', SS', 'STRIP'].

python detect_analysis.py --exp_path $exp_path \
                          --exp_config $exp_config \
                          --exp_name $exp_name \
                          --gamma $gamma

Citation

If you use this code in your work, please cite the accompanying paper:

@inproceedings{
huang2023distilling,
title={Distilling Cognitive Backdoor Patterns within an Image},
author={Hanxun Huang and Xingjun Ma and Sarah Monazam Erfani and James Bailey},
booktitle={ICLR},
year={2023},
}

Acknowledgements

This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200. The authors would like to thank Yige Li for sharing the several triggers used in the experiments.

Part of the code is based on the following repo:

• Dynamic Attack: https://github.com/VinAIResearch/input-aware-backdoor-attack-release
• Spectral Signatures: https://github.com/MadryLab/backdoor_data_poisoning
• STRIP: https://github.com/garrisongys/STRIP
• NAD: https://github.com/bboylyg/NAD
• ABL: https://github.com/bboylyg/ABL
• ISSBA: https://github.com/yuezunli/ISSBA
• Frequency: https://github.com/YiZeng623/frequency-backdoor
• https://github.com/JonasGeiping/data-poisoning/blob/main/forest/filtering_defenses.py